1. Field of Invention
The present invention generally relates to biometric identification. More specifically, the present invention relates to face recognition or identification verification purposes.
2. Description of Related Art
Biometrics refers to the use of intrinsic human traits for personal identification purposes. That is, a person may be identified by one or a combination of multiple different personal traits characteristic of that person. Examples of such personal traits are a fingerprint, a hand print (length and thickness of the fingers, size of the hand itself), a retina scan (pattern of blood vessels in the eye), an iris scan, a facial photograph, a blood vessel pattern (vein pattern), a voice print, a dynamic signature (the shape and time pattern for writing a signature), or a keystroke pattern (key entry timing).
Of particular interest are biometric identification techniques that use images for personal identification. The images may be of any of a number of biometric types. For example, the images used may be of finger prints, blood vessel patterns, iris scans, etc. Of particular interest, however is the use of facial photographs for deification verification. This topic relates to the field of computer vision.
In the field of computer vision, it is generally desirable that an image not only be captured, but that a computer is able to identify and label various features within the captured image. Basically, a goal of computer vision is for the computer to “understand” the content of a captured image.
Various approaches to identifying features within a captured image are known. Early approaches centered on the concept of identifying shapes. For example, if a goal was to identify a specific item, such as wrench or a type of wrench, then a library of the different types of acceptable wrenches (i.e. “true examples” defined as images of “true” wrenches) would be created. The outline shapes of the wrenches within these true examples would be stored, and a search for the acceptable shapes would be conducted on a captured image. This approach of shape searching was successful when one had an exhaustive library of acceptable shapes, the library was not overly large, and the subject of the captured images did not deviate from the predefined true shapes.
However for complex searches, such as searching an image for biometric identifiers that are prone to change, this exhaustive library approach is not practical. For example, a human face has definite characteristics, but does not have an easily definable number of shapes and/or appearances it may adopt. It is to be understood that the term appearance is herein used to refer to color and/or light differences across an object, as well as other surface/texture variances. The difficulties in understanding a human face becomes even more acute when one considers that it is prone to shape distortion and/or change in appearance within the normal course of human life due to changes in emotion, expression, speech, age, etc. It is self-apparent that compiling an exhaustive library of human faces and their many variations is a practical impossibility.
Recent developments in image recognition of objects that change their shape and appearance, such as a human face, are discussed in “Statistical Models of Appearance for Computer Vision”, by T. F. Cootes and C. J. Taylor (hereinafter Cootes et al.), Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PT, U.K. email: t.cootes@man.ac.uk, http://www.isbe.man.ac.uk, Mar. 8, 2004, which is hereby incorporated in its entirety by reference.
Cootes et al. suggest that in order for a machine to be able to understand what it “sees”, it must make use of models that describe and label the expected structure being imaged. In the past, model-based vision has been applied successfully to images of man-made objects, but their use has proven more difficult in interpreting images of natural subjects, which tend to be complex and variable. The main problem is the variability of the subject being examined. To be useful, a model needs to be specific, that is, it should represent only true examples of the modeled subject. To identify a variable object, however, the model needs to be general and represent any plausible true example of the class of object it represents. Recent developments have shown that this apparent contradiction can be handled by statistical models that can capture specific patterns of variability in shape and appearance. It has further been shown that these statistical models can be used directly in image interpretation. That is, they may be used to identify a specific example of a general object class within an image.
Another area of image recognition is feature detection, and its use in correspondence matching. Feature detection is used to correlate similar features of an object in different images. That is, if each of two images has a different view of a common object, feature detection permits one to identify the corresponding portions of the common object that are visible in the two images. Thus, given a first view of an object in a first image, one can look for, and identify, the same object in another image, even if the other image provides only a partial, or skewed, view of the same object.
Feature detection often relies on the use of feature points that can be uniquely identified and correlated in two, or more, images. The feature points are typically pixels (or point regions within an image) that are uniquely identifiable by a set of statistical characteristics determined from neighboring pixels within a defined neighborhood surrounding the feature point.
Correspondence matching (or the correspondence problem) refers to the matching of objects (or object features or feature points) common to two, or more, images. Correspondence matching tries to figure out which parts of a first image correspond to (i.e. are matched to) which parts of a second image, assuming that the second image was taken after the camera had moved, time had elapsed, and/or the pictured objects had moved. For example, the first image may be of a real-world scene taken from a first view angle with a first field of vision, FOV, and the second image may be of the same scene taken from a second view angle with a second FOV. Assuming that the first and second FOVs at least partially overlap, correspondence matching refers to the matching of common features points in the overlapped portions of the first and second images.
For example in FIG. 1, images 2, 4, 6 and 8 each provide partial, and overlapping, views of a building in a real-world scene, but none provide a full view of the entire building. However, by applying edge detection and indexing (i.e. identifying matching pairs of) feature points in the four partial images 2, 4, 6 and 8 that correlate to the same real feature point in the real-world scene, it is possible to identify corresponding features in the four partial images 2-8 and stitch together the four partial images (i.e. applying an image stitching tool) to create one composite image 10 of the entire building. In the present example, the four partial images 2-8 are taken from the same view angle, but this approach may be extended to images of a common scene that are taken from different view angles.
Feature detection may also be used to look for specific objects in image. For example, if one has a library of identifying features of a specific object, then one may search a test image for those identifying features in an effort to determine if an example of the specific object is present in the test image. When this is extended to multiple test images of a common scene taken from different view angles, it is possible to index, i.e. match or correlate, feature points from one image to the other.
It is an object of the present invention to provide a biometric identification system that provides for quick identification of a submitted test image.
It is another object of the present invention to provide a face recognition identification system that combines the use of statistical models to handle variations in an object class, with the simplicity of using feature points to correlate features of different image.